Enhanced brain-machine interfaces with neuromodulation

ABSTRACT

Described is an improved brain-machine interface including a neural interface and a controllable device in communication with the neural interface. The neural interface includes a neural device with one or more sensors for collecting signals of interest and one or more processors for conditioning the signals of interest, extracting salient neural features from and decoding the conditioned signals of interest, and generating a control command for the controllable device. The controllable device performs one or more operations according to the control command, and the neural device administers neuromodulation stimulation to reinforce operation of the controllable device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a Continuation-in-Part Application of U.S. application Ser. No.15/332,787, filed in the United States on Oct. 24, 2016, entitled,“Method and System to Accelerate Consolidation of Specific MemoriesUsing Transcranial Stimulation,” which is a Non-Provisional patentapplication of 62/245,730, filed in the United States on Oct. 23, 2015,entitled, “Method and System to Accelerate Consolidation of SpecificMemories Using Transcranial Stimulation,” the entirety of which arehereby incorporated by reference.

This is ALSO a Non-Provisional Application of U.S. ProvisionalApplication No. 62/712,447, filed in the United States on Jul. 31, 2018,entitled, “Enhanced Brain-Machine Interfaces with Neuromodulation,” theentirety of which is incorporated herein by reference.

GOVERNMENT LICENSE RIGHTS

This invention was made with government support under U.S. GovernmentContract Number W911NF-16-C-0018. The government has certain rights inthe invention.

BACKGROUND OF INVENTION (1) Field of Invention

The present invention relates to an enhanced brain-machine interface,and more particularly, to an enhanced brain-machine interface that usesneuromodulation.

(2) Description of Related Art

A brain-machine interface is a direct communication pathway between anenhanced or wired brain and an external device. A brain-machineinterface can be used for researching, mapping, assisting, augmenting,or repairing human cognitive or sensory-motor functions.

There is a lack of prior art directly addressing the enhancement ofneural interfaces through the utilization of transcranial stimulation,intracranial stimulation, or other neuromodulatory signals. The closestexisting prior art that addresses the enhancement of neural control wasperformed by Pan et al. (see Literature Reference No. 7 in the List ofIncorporated Literature References) and focused on using transcranialdirect current stimulation to improve the electromyographic response atthe periphery.

Coleman (U.S. Publication No. 2015/0351655, which is hereby incorporatedby reference as though fully set forth herein) described the directmeasurement of neural activity using electroencephalogram (EEG) andmanipulation of the neural state by providing direct visual or auditoryfeedback. This technique is severely limited in its ability todiscriminate between different neural processes, or produce a rich setof control outputs. Beyond neuromodulation, other techniques forimproving brain-machine interfaces have focused on the improvement ofsensor readings and improving decoding techniques.

All prior approaches that utilize neuromodulation focus on utilizing itfor improvement of cognitive tasks or physical therapy. The primaryreason for this is the limited amount of research in the field. Otherprior approaches to improving brain-machine interfaces have been sensorand decoder-centric. However, in addressing accuracy of the neuralinterface, more advanced decoders that perform well over an extendedduration without retraining or recalibration to the subject require asignificant amount of data.

Thus, a continuing need exists for a brain-machine interface thatutilizes stimulation to improve the neurophysiological response in thebrain, not the periphery, and then utilizes that signal to directlycontrol a machine.

SUMMARY OF INVENTION

The present invention relates to an enhanced brain-machine interface,and more particularly, to an enhanced brain-machine interface that usesneuromodulation. The enhanced brain-machine interface comprises a neuralinterface and a controllable device in communication with the neuralinterface. The neural interface comprises a neural device having one ormore sensors for collecting signals of interest, wherein the neuraldevice is configured to administer neuromodulation stimulation, and oneor more processors and a non-transitory computer-readable medium havingexecutable instructions encoded thereon such that when executed, the oneor more processors perform operations of conditioning the signals ofinterest, resulting in conditioned signals of interest; extractingsalient neural features from the conditioned signals of interest;decoding the salient neural features, providing a mapping between aninput neural feature space and an output control space for thecontrollable device; based on the mapping, generating at least onecontrol command for the controllable device; causing the controllabledevice to perform one or more operations according to the at least onecontrol command; and causing the neural device to administerneuromodulation stimulation to reinforce operation of the controllabledevice.

In another aspect, the signals of interest comprise at least one ofneural signals and environmental signals.

In another aspect, the neuromodulation stimulation is one of auditory,visual, and electrical stimulation.

In another aspect, the neuromodulation stimulation comprises uniquespatiotemporal amplitude-modulated patterns (STAMPs) of stimulation.

In another aspect, the controllable device is a prosthetic limb.

In another aspect, the one or more neural sensors comprises one or moreelectrodes configured to perform at least one of sensing and applyingstimulation.

Finally, the present invention also includes a computer program productand a computer implemented method. The computer program product includescomputer-readable instructions stored on a non-transitorycomputer-readable medium that are executable by a computer having one ormore processors, such that upon execution of the instructions, the oneor more processors perform the operations listed herein. Alternatively,the computer implemented method includes an act of causing a computer toexecute such instructions and perform the resulting operations.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects, features and advantages of the present invention will beapparent from the following detailed descriptions of the various aspectsof the invention in conjunction with reference to the followingdrawings, where:

FIG. 1 is a block diagram depicting the components of an improvedbrain-machine interface according to some embodiments of the presentdisclosure;

FIG. 2 is an illustration of a computer program product according tosome embodiments of the present disclosure;

FIG. 3 is a diagram illustrating a system for neuromodulation inprosthetic control according to some embodiments of the presentdisclosure; and

FIG. 4 is a diagram illustrating control of a brain-machine interfaceaccording to some embodiments of the present disclosure.

DETAILED DESCRIPTION

The present invention relates to an improved brain-machine interface,and more particularly, to an improved brain-machine interface that usesneuromodulation. The following description is presented to enable one ofordinary skill in the art to make and use the invention and toincorporate it in the context of particular applications. Variousmodifications, as well as a variety of uses in different applicationswill be readily apparent to those skilled in the art, and the generalprinciples defined herein may be applied to a wide range of aspects.Thus, the present invention is not intended to be limited to the aspectspresented, but is to be accorded the widest scope consistent with theprinciples and novel features disclosed herein.

In the following detailed description, numerous specific details are setforth in order to provide a more thorough understanding of the presentinvention. However, it will be apparent to one skilled in the art thatthe present invention may be practiced without necessarily being limitedto these specific details. In other instances, well-known structures anddevices are shown in block diagram form, rather than in detail, in orderto avoid obscuring the present invention.

The reader's attention is directed to all papers and documents which arefiled concurrently with this specification and which are open to publicinspection with this specification, and the contents of all such papersand documents are incorporated herein by reference. All the featuresdisclosed in this specification, (including any accompanying claims,abstract, and drawings) may be replaced by alternative features servingthe same, equivalent or similar purpose, unless expressly statedotherwise. Thus, unless expressly stated otherwise, each featuredisclosed is one example only of a generic series of equivalent orsimilar features.

Furthermore, any element in a claim that does not explicitly state“means for” performing a specified function, or “step for” performing aspecific function, is not to be interpreted as a “means” or “step”clause as specified in 35 U.S.C. Section 112, Paragraph 6. Inparticular, the use of “step of” or “act of” in the claims herein is notintended to invoke the provisions of 35 U.S.C. 112, Paragraph 6.

Before describing the invention in detail, first a list of citedreferences is provided. Next, a description of the various principalaspects of the present invention is provided. Finally, specific detailsof various embodiment of the present invention are provided to give anunderstanding of the specific aspects.

(1) LIST OF INCORPORATED LITERATURE REFERENCES

The following references are cited and incorporated throughout thisapplication. For clarity and convenience, the references are listedherein as a central resource for the reader. The following referencesare hereby incorporated by reference as though fully set forth herein.The references are cited in the application by referring to thecorresponding literature reference number, as follows:

-   -   1. Astaras, Alexander, et al. “Towards brain-computer interface        control of a 6-degree-of-freedom robotic arm using dry EEG        electrodes.” Advances in Human-Computer Interaction 2013 (2013):        2.    -   2. Mankin, Emily A., et al. “Hippocampal CA2 activity patterns        change over time to a larger extent than between spatial        contexts.” Neuron 85.1 (2015): 190-201.    -   3. Chi, Zhiyi, and Daniel Margoliash. “Temporal precision and        temporal drift in brain and behavior of zebra finch song.”        Neuron 32.5 (2001): 899-910.    -   4. Wheeler, Kevin R., and Charles C. Jorgensen. “Gestures as        input: Neuroelectric joysticks and keyboards.” IEEE pervasive        computing 2.2 (2003): 56-61.    -   5. Bashivan, Pouya, et al. “Learning representations from EEG        with deep recurrent-convolutional neural networks.” arXiv        preprint arXiv:1511.06448 (2015).    -   6. Elango, Venkatesh, et al. “Sequence Transfer Learning for        Neural Decoding.” bioRxiv (2017): 210732.    -   7. Pan, Lizhi, et al, “Transcranial direct current stimulation        versus user training on improving online myoelectric control for        amputees.” J. Neural Eng 14.046019 (2017): 046019.    -   8. Patel, Aashish, et al. “Mental state assessment and        validation using personalized physiological biometrics.” Front.        Hum. Neurosci. 2018. Vol. 12, Article 221.

(2) PRINCIPAL ASPECTS

Various embodiments of the invention include three “principal” aspects.The first is a system for an improved brain-machine interface. Thesystem is typically in the form of a computer system operating softwareor in the form of a “hard-coded” instruction set. This system may beincorporated into a wide variety of devices that provide differentfunctionalities. The second principal aspect is a method, typically inthe form of software, operated using a data processing system(computer). The third principal aspect is a computer program product.The computer program product generally represents computer-readableinstructions stored on a non-transitory computer-readable medium such asan optical storage device, e.g., a compact disc (CD) or digitalversatile disc (DVD), or a magnetic storage device such as a floppy diskor magnetic tape. Other, non-limiting examples of computer-readablemedia include hard disks, read-only memory (ROM), and flash-typememories. These aspects will be described in more detail below.

A block diagram depicting an example of a system (i.e., computer system100) of the present invention is provided in FIG. 1. The computer system100 is configured to perform calculations, processes, operations, and/orfunctions associated with a program or algorithm. In one aspect, certainprocesses and steps discussed herein are realized as a series ofinstructions (e.g., software program) that reside within computerreadable memory units and are executed by one or more processors of thecomputer system 100. When executed, the instructions cause the computersystem 100 to perform specific actions and exhibit specific behavior,such as described herein.

The computer system 100 may include an address/data bus 102 that isconfigured to communicate information. Additionally, one or more dataprocessing units, such as a processor 104 (or processors), are coupledwith the address/data bus 102. The processor 104 is configured toprocess information and instructions. In an aspect, the processor 104 isa microprocessor. Alternatively, the processor 104 may be a differenttype of processor such as a parallel processor, application-specificintegrated circuit (ASIC), programmable logic array (PLA), complexprogrammable logic device (CPLD), or a field programmable gate array(FPGA).

The computer system 100 is configured to utilize one or more datastorage units. The computer system 100 may include a volatile memoryunit 106 (e.g., random access memory (“RAM”), static RAM, dynamic RAM,etc.) coupled with the address/data bus 102, wherein a volatile memoryunit 106 is configured to store information and instructions for theprocessor 104. The computer system 100 further may include anon-volatile memory unit 108 (e.g., read-only memory (“ROM”),programmable ROM (“PROM”), erasable programmable ROM (“EPROM”),electrically erasable programmable ROM “EEPROM”), flash memory, etc.)coupled with the address/data bus 102, wherein the non-volatile memoryunit 108 is configured to store static information and instructions forthe processor 104. Alternatively, the computer system 100 may executeinstructions retrieved from an online data storage unit such as in“Cloud” computing. In an aspect, the computer system 100 also mayinclude one or more interfaces, such as an interface 110, coupled withthe address/data bus 102. The one or more interfaces are configured toenable the computer system 100 to interface with other electronicdevices and computer systems. The communication interfaces implementedby the one or more interfaces may include wireline (e.g., serial cables,modems, network adaptors, etc.) and/or wireless (e.g., wireless modems,wireless network adaptors, etc.) communication technology.

In one aspect, the computer system 100 may include an input device 112coupled with the address/data bus 102, wherein the input device 112 isconfigured to communicate information and command selections to theprocessor 100. In accordance with one aspect, the input device 112 is analphanumeric input device, such as a keyboard, that may includealphanumeric and/or function keys. Alternatively, the input device 112may be an input device other than an alphanumeric input device. In anaspect, the computer system 100 may include a cursor control device 114coupled with the address/data bus 102, wherein the cursor control device114 is configured to communicate user input information and/or commandselections to the processor 100. In an aspect, the cursor control device114 is implemented using a device such as a mouse, a track-ball, atrack-pad, an optical tracking device, or a touch screen. The foregoingnotwithstanding, in an aspect, the cursor control device 114 is directedand/or activated via input from the input device 112, such as inresponse to the use of special keys and key sequence commands associatedwith the input device 112. In an alternative aspect, the cursor controldevice 114 is configured to be directed or guided by voice commands.

In an aspect, the computer system 100 further may include one or moreoptional computer usable data storage devices, such as a storage device116, coupled with the address/data bus 102. The storage device 116 isconfigured to store information and/or computer executable instructions.In one aspect, the storage device 116 is a storage device such as amagnetic or optical disk drive (e.g., hard disk drive (“HDD”), floppydiskette, compact disk read only memory (“CD-ROM”), digital versatiledisk (“DVD”)). Pursuant to one aspect, a display device 118 is coupledwith the address/data bus 102, wherein the display device 118 isconfigured to display video and/or graphics. In an aspect, the displaydevice 118 may include a cathode ray tube (“CRT”), liquid crystaldisplay (“LCD”), field emission display (“FED”), plasma display, or anyother display device suitable for displaying video and/or graphic imagesand alphanumeric characters recognizable to a user.

The computer system 100 presented herein is an example computingenvironment in accordance with an aspect. However, the non-limitingexample of the computer system 100 is not strictly limited to being acomputer system. For example, an aspect provides that the computersystem 100 represents a type of data processing analysis that may beused in accordance with various aspects described herein. Moreover,other computing systems may also be implemented. Indeed, the spirit andscope of the present technology is not limited to any single dataprocessing environment. Thus, in an aspect, one or more operations ofvarious aspects of the present technology are controlled or implementedusing computer-executable instructions, such as program modules, beingexecuted by a computer. In one implementation, such program modulesinclude routines, programs, objects, components and/or data structuresthat are configured to perform particular tasks or implement particularabstract data types. In addition, an aspect provides that one or moreaspects of the present technology are implemented by utilizing one ormore distributed computing environments, such as where tasks areperformed by remote processing devices that are linked through acommunications network, or such as where various program modules arelocated in both local and remote computer-storage media includingmemory-storage devices.

An illustrative diagram of a computer program product (i.e., storagedevice) embodying the present invention is depicted in FIG. 2. Thecomputer program product is depicted as floppy disk 200 or an opticaldisk 202 such as a CD or DVD. However, as mentioned previously, thecomputer program product generally represents computer-readableinstructions stored on any compatible non-transitory computer-readablemedium. The term “instructions” as used with respect to this inventiongenerally indicates a set of operations to be performed on a computer,and may represent pieces of a whole program or individual, separable,software modules. Non-limiting examples of “instruction” includecomputer program code (source or object code) and “hard-coded”electronics (i.e. computer operations coded into a computer chip). The“instruction” is stored on any non-transitory computer-readable medium,such as in the memory of a computer or on a floppy disk, a CD-ROM, and aflash drive. In either event, the instructions are encoded on anon-transitory computer-readable medium.

(3) SPECIFIC DETAILS OF VARIOUS EMBODIMENTS

Described is an improved brain-machine interface (BMI), which enhancesthe efficacy of current brain-machine interfaces. Particularly, theimproved BMI according to embodiments of the present disclosureaddresses the challenges of: 1) learning to control devices controlledby neural activity, and 2) maintaining effective neural control ofdevices over extended use. The system is comprised of a portable systemthat integrates neural signal measurements (e.g., electroencephalogram,functional near infrared spectroscopy, etc.) and a transcranialstimulator (i.e., alternating current, direct current, focusedultrasound, photoacoustics) for use during constrained or unconstrainedscenarios, and in open- or closed-loop configuration. Note that atranscranial stimulator can also apply sensory stimuli, such as auditoryor visual cues. The closed-loop operation determines the transcranialstimulator parameters (e.g., phase, frequency) based on ongoing brainstates (e.g., slow-wave oscillations in scalp electroencephalogramduring sleep), as opposed to open-loop operation, which does not rely onfeedback from ongoing brain states.

Leveraging recent insights from sleep stimulation work and memory,neuromodulation can be applied through STAMP (spatiotemporalamplitude-modulated patterns) tagging of desired behavior during activeperiods and consolidation during sleep (as described in U.S. applicationSer. No. 15/332,787, which is hereby incorporated by reference as thoughfully set forth herein), as well as through other neuromodulatorymodalities, including auditory or visual cues. The invention describedherein enables individuals utilizing neural interfaces to achieve ahigher degree of freedom (DOF) output system control, to improvestability of the neural interface as a whole during extended durationuse, and to decrease the amount of training time necessary to learncontrol modulation. The majority of systems that provide both sensingand control interfaces address limitations in control through therestriction of decoded output to a handful of reliable signals (seeLiterature Reference No. 1). Beyond the limitation of the throughput andcontrol bits available, the prior technique is non-intuitive andtime-consuming for users as they require extensive chaining ofprimitives to perform complex tasks or the use of more intuitivemodalities, such as electromyography (EMG), that require leeching offanother extremity (see Literature Reference No. 2).

Furthermore, extended duration use of any neural interface makes it lessrobust and requires repeated calibration to be performed throughout theday to ensure accurate and reliable function (see Literature ReferenceNos. 3 and 4). While improvements in neural decoders are being made toaddress this issue as well as improving overall performance (seeLiterature References Nos. 5 and 6), calibration remains the mostreliable solution in lieu of extensive amounts of subject-specificneural data.

Lastly, learning to control neural interfaces is difficult as itrequires manipulating a process that normally operates invisibly andeffortlessly day-to-day. Existing mitigation techniques rely on trainingindividuals to induce increased neural activity indirectly by thinkingabout specific tasks or objects, or directly by actively engaging with asystem, such as a videogame. While effective for a few control outputs,this becomes difficult to leverage for numerous control outputs and,consequently, generalizes poorly. By directly addressing key limitationsin neural interfaces, the system according to embodiments of the presentdisclosure enables effective, high degree-of-freedom (DOF) utilizationof neural interfaces in a host of applications ranging from prosthesesto games.

The following is a description of the application of an individualutilizing the brain-machine interface described herein to control aprosthetic device, such as a robotic arm. This application is selectedas an example due to the large number of control parameters required tofully articulate an upper body prosthetic. Moreover, due to the typicalheavy use of the prostheses, this interface necessitates long-termstability of the decoders utilized.

FIG. 3 depicts a non-limiting example of a system (e.g., portabledevice) implementing the invention described herein for controlling aprosthetic limb 300. The core system is composed of measurement,processing, and communication components. The measurements may beperformed with electroencephalography (EEG) electrodes, or any otherinvasive or non-invasive neural recording technology, such as functionalnear infrared spectroscopy (fNIRS) and electrocorticography (ECoG).These sensors would be utilized in a portable headset 302 or otherhead-mounted platform, such as a neural headcap or headgear. Forexample, the portable headset 302 can be a headcap containing sensors todetect high-resolution spatiotemporal neurophysiological activity. Theportable headset 302 can also include stimulation elements for directingcurrent flow to specific neural regions. It should be understood thatadditional headgear configurations can also be implemented, such as anon-elastic headcap, nets (such as hair or head nets), bands, visors,helmets, or other headgear.

In one aspect, the neural interface and intervention system describedherein comprises one or more electrodes or transducers (electrical,magnetic, optical, ultrasonic) in contact with the head, which iscapable of sensing and/or applying stimulation. The one or moreelectrodes can be non-invasive (e.g., surface of head) or invasive(e.g., brain). The electrodes (or other neural sensors) measuredifferences in voltage between neurons in the brain. The signal is thenprocessed (e.g., amplified, filtered) as described below, thenautomatically interpreted by a computer program to generate controlcommands for the machine (e.g., prosthetic limb).

In one embodiment, a portable device collects neural signals of interestas described above. The portable device could also take input from theenvironment through other sensors attached with the portable device(e.g., global positioning system (GPS) coordinates, accelerometer data,gyro sensor data) to mark behavioral or neural events of interest anddetermine the appropriate stimulation patterns. In another aspect, aportable device both collects and processes signals of interest as wellas administers neuromodulation to the brain. In yet another embodiment,a portable device may collect and process signals, applyneuromodulation, and interface with a controllable system (such as aprosthetic arm or a remote robot). The collected signals are then, inreal-time, processed by a portable processing component (either adedicated device, or through the use of a mobile application) thatconditions the neural data and performs neural feature extraction 304 toextract the salient neural features.

Furthermore, as novel decoders are created regularly, the processingcomponent supports the integration of on-board or remote co-processingmodules that assist in interpreting the neural output. Moreover, asnoted above, the processing component also takes input to markbehavioral or neural events of interest and determine the appropriatestimulation patterns. The stimulation patterns, when electrical, willfollow U.S. application Ser. No. 15/332,787. Briefly, the approach iscomposed of applying unique spatiotemporal amplitude-modulated patterns(STAMPs) of stimulation (e.g., transcranial current stimulation (tCS)306) to tag and cue memories, applied via a suitable stimulation deviceto salient physiological regions depending on the task the user wishesto perform (e.g., controlling a prosthetic arm to reach for an object orto grasp and lift an object). The brain regions involved in the planningand execution of individual tasks, if known from the functionalneuroimaging of the user's brain, can be accordingly targeted withSTAMPs. Other neuromodulatory techniques can be applied as appropriate.

Lastly, the communication component provides the ability to manipulateexternal systems through the BMI 308. While the interface for thecontrollable device requires an application programming interface (API)or other analog/digital interface, the invention described hereinprovides a standard communication layer to access the trained andprocessed brain activity in the form of discrete or continuous controlsignals. A system diagram depicting the different components of thesystem according to embodiments of the present disclosure is shown inFIG. 4. The following is a detailed description of the subsystems andtheir implementation.

(3.1) Signal Conditioning 400

Upon receiving a neural input 402 from a brain 403 from, for instance,electrodes (EEG, ECoG, fNIRS), the signal conditioning component 400 ofthe invention provides the feature extraction component 404 and theneural decoder component 406 with a clean underlying signal. Utilizingrecent research conducted at HRL Laboratories (see Literature ReferenceNo. 8) as well as examining literature for best practices inconditioning, the following set of steps provides a sampleimplementation for signal conditioning.

-   -   1. Remove common channel noise if the measurement modality        requires it. Situations where this may be required would be        where noise is introduced into other channels due to poor wire        harness isolation, or there is a common external noise generator        present in all channels.    -   2. Correct signal drift. This process involves removing the        linear trend from the signal vector to ensure an average signal        slope of ˜0.    -   3. Where necessary, create virtual channels to provide better        spatial representation of neural activation in the feature        space. Alternatively, independent component analysis (ICA) can        be utilized to extract the signal components from the shared        (noisy) neural recording space to provide a source-centric        signal for use in the feature extraction component 404.

(3.2) Feature Extraction 404

The feature extraction component 404 of the system diagram (FIG. 4)provides the neural decoder component 406 with signal components thatare a rich summary of the neural temporal signals. This can includeanything from signal power in physiological ranges of interest (e.g.,stereotypical delta, alpha, beta, theta, gamma, and/or high-gammaranges) to unsupervised features extracted using autoencoders. EEG is arepresentative source of signals. An autoencoder is a neural networkthat can be trained to learn a compressed representation of the inputs.A subject expert is used to identify the features of interest andprovide the algorithms to extract them for use down-stream. For example,a subject expert may suggest beta-power and signal coherence be utilizedfor gross motor movement, or high-gamma power for higher-order cognitivedecoding, and even using beta-high-gamma- and signal coherence forspeech decoding.

(3.3) Neural Decoder 406

The neural decoder component 406 is the second-to-last step in thebrain-machine interface 308; it provides a learned mapping between theinput neural feature space and the output application control space.While any state-of-the-art algorithm may be utilized for providing thislearned projection (i.e., Conv-LSTMs (convolutional long short-termmemories), RNNs (recurrent neural networks), and linear models such asLDA (Linear Discriminant Analysis)), the innovation stems from theneuromodulation stimulation 408.

In a typical use case, a prior art neural decoder component is trainedon a set of data and then requires recalibration when signal dynamics orquality change. In contrast, the neuromodulation stimulation 408according to embodiments of the present disclosure providesreinforcement to the user via stimulation (e.g., electrical, auditory,visual) during training and during normal use to ensure neural decoder406 stability and enhanced accuracy. The stability, in particular, isinduced by the reinforcement of particular neural dynamics via externalinnervation. The neural decoder 406 accuracy, consequently, is exhibitedvia the stereotypical neuronal activation pathway resulting from thestimulation reinforcement (i.e., neuromodulation stimulation 408). Whilestimulation during training would follow a strict regimen to ensurerapid adaptation to the controller, the testing reinforcement wouldoccur during high-confidence decoding intervals where a fluctuation insignal characteristics are observed prior (i.e., signal-to-noisechanges, signal dynamics changes).

(3.3.1) Neurostimulation/Neuromodulatory Feedback

While the modality of the neurostimulatory/neuromodulatory feedback vianeuromodulation stimulation 408 can be one of many choices, includingelectrical, auditory, and optical, the STAMP technique can also beutilized. In particular, the initial training setup (during waking)would be composed of a closed-loop performance monitoring system thatwould measure the user's performance on a desired task and apply STAMPstimulation to tag behavioral events. During sleep, the brain activitypatterns underlying desired behaviors (e.g., correct operation ormovement of the prosthetic arm in a particular trial) can be reactivatedusing the application of relevant STAMPs to promote long-term stabilityand consolidation. Depending on the task, the stimulation may be focalto brain regions of interest (e.g., sensorimotor cortex) orregion-indiscriminate stimulation for more complex behaviors. Once abaseline performance that is acceptable to the user is achieved, thededicated training process is complete.

During normal use, the user or a teacher will manually tag behaviorsthat are desirable by applying unique STAMPs from start to finish ofbehavioral sequences. Upon sleeping, the user would receive simulationthat would again reinforce the desired behaviors with respect to thecontrollable device (e.g., prosthetic arm). Furthermore, inhigh-cognitive load environments, the system would be able to providepreemptive stimulation in real-time to reinforce the neural dynamics fora particular behavior decreasing the pathway threshold for a desiredbehavior to be exhibited. This type of use would support controller usein conditions where mental fatigue is adversely affecting the decoderperformance.

As described above, the invention described herein provides the abilityto manipulate external systems through the brain-machine interface 308.The standard communication layer 410 transmits the decoded parametersfrom the processed brain activity in the form of discrete or continuouscontrol signals for operating the different actuators of thecontrollable device (e.g., joints of a prosthetic arm). Thecommunication layer 410 controls output from the neural interface(wireless or wired, and digital or analog) that is supplied to themachine that the individual is controlling.

As described above, a unique aspect of the invention described herein isthe use of the neuromodulation stimulation 408 to tag desirablebehaviors during waking and cue them during sleep as reinforcement forthem to be seamlessly used in controlling external devices/machines 412(e.g., prosthetic limb, remote robot), as well as improving therobustness of such a system by enhancing the neural repeatability ofsignals involved with conditioning. Multiple usage scenarios exist forthe system according to embodiments of the present disclosure, but twoprimary techniques are described: classical train-test and onlinetrain-test. The classical approach is what is typically used to trainneural interfaces. A user performs the desired task numerous times andthe neural data is used to train a decoder offline. Once enough data iscollected for the desired behavior and the neural decoder is capable ofachieving some performance, the user may use the interface (typicallyfor a few hours) before requiring recalibration. Applying the inventiondescribed herein to this approach, an automated or manual approach fortagging desirable behaviors during training is performed and thestimulation is applied accordingly.

The online train-test approach is more flexible in its expandability ofcontrol primitives with the caveat of requiring user intervention duringuse. In this approach, a user is trained classically on a set of baseprimitives. During regular use, however, the user can add more neuralresponses for use in the control dictionary. The user would simply useone of the base primitives already trained, or using a physical inputsuch as a button force the system to use the last few instances in timeas favorable behavior for a new or reinforced control signal. Thisapproach allows for more gradual learning as opposed to the learning ofall control signals at once in the classical technique described above.

Both the classical and online techniques for learning the controlprimitives can be applied in an open-loop and closed-loop manner. Theopen-loop approach simply does not allow the user to retrain the modelonce the models are trained to achieve a certain performance. Theclosed-loop approach allows for stimulation to occur to condition theneural response during regular use and can better control the neuraldynamics robustness over time.

The closest existing prior art that addresses the enhancement ofbrain-machine interfaces was performed by Pan et al. (see LiteratureReference No. 7) and focused on using transcranial direct currentstimulation to improve the electromyographic response of the peripheralmuscles rather than the central nervous system. This is a sharp contrastto the system described herein in that the system utilizes stimulationto improve the neurophysiological response in the brain (i.e., thecentral nervous system), not the peripheral muscles, and then utilizesthat signal to directly control a machine. Beyond neuromodulation, othertechniques for improving brain-machine interfaces have focused on theimprovement of sensor readings and improving decoding techniques. Whilethese improvements are important, the present invention can utilize notonly these advances, but also provide more conditioned neural responsesto these sensors and decoders by manipulating the underlying neuraldynamics. No comparable techniques exist for improving brain-machineinterfaces through the use of neuromodulation. All prior approaches thatutilize neuromodulation focus on utilizing it for improvement ofcognitive tasks or physical therapy. The primary reason for this is thelimited amount of research in the field; as such, the inventiondescribed herein leverages recent advances that allow this technique tobe utilized effectively. Other prior approaches to improvingbrain-machine interfaces have been sensor and decoder-centric. Assensors improve the performance of all neural interfaces, they are anorthogonal comparison; however, the decoder improvements address some ofthe challenges addressed by the present invention. However, inaddressing accuracy of the neural interface, more advanced decoders thatperform well over an extended duration without retraining orrecalibration to the subject require a significant amount of data. Assuch, the invention provides an approach that not only addresses thechallenges of existing brain-machine interfaces, but also retains theadvantages afforded by other advances in neural interface work.

While numerous applications exist in the medical, commercial, anddefense spaces, a potential application is medicine with a focus onprosthetic control (i.e., supporting the recovery of veterans who havelost limbs). Other applications include controlling an exoskeleton forable-bodied individuals to perform super-human tasks, and a remote robotperforming search-and-rescue operations in dangerous circumstances.

Finally, while this invention has been described in terms of severalembodiments, one of ordinary skill in the art will readily recognizethat the invention may have other applications in other environments. Itshould be noted that many embodiments and implementations are possible.Further, the following claims are in no way intended to limit the scopeof the present invention to the specific embodiments described above. Inaddition, any recitation of “means for” is intended to evoke ameans-plus-function reading of an element and a claim, whereas, anyelements that do not specifically use the recitation “means for”, arenot intended to be read as means-plus-function elements, even if theclaim otherwise includes the word “means”. Further, while particularmethod steps have been recited in a particular order, the method stepsmay occur in any desired order and fall within the scope of the presentinvention.

What is claimed is:
 1. An enhanced brain-machine interface withneuromodulation, the enhanced brain-machine interface comprising: aneural interface and a controllable device in communication with theneural interface, wherein the neural interface comprises: a neuraldevice having one or more sensors for collecting signals of interest,wherein the neural device is configured to administer neuromodulationstimulation; and one or more processors and a non-transitorycomputer-readable medium having executable instructions encoded thereonsuch that when executed, the one or more processors perform an operationof: conditioning the signals of interest, resulting in conditionedsignals of interest; extracting salient neural features from theconditioned signals of interest; decoding the salient neural features,providing a mapping between an input neural feature space and an outputcontrol space for the controllable device; based on the mapping,generating at least one control command for the controllable device;causing the controllable device to perform one or more operationsaccording to the at least one control command; and causing the neuraldevice to administer neuromodulation stimulation to reinforce operationof the controllable device.
 2. The system as set forth in claim 1,wherein the signals of interest comprise at least one of neural signalsand environmental signals.
 3. The system as set forth in claim 1,wherein the neuromodulation stimulation is one of auditory, visual, andelectrical stimulation.
 4. The system as set forth in claim 3, whereinthe neuromodulation stimulation comprises unique spatiotemporalamplitude-modulated patterns (STAMPs) of stimulation.
 5. The system asset forth in claim 1, wherein the controllable device is a prostheticlimb.
 6. The system as set forth in claim 1, wherein the one or moreneural sensors comprises one or more electrodes configured to perform atleast one of sensing and applying stimulation.
 7. A method forimplementing an enhanced brain-machine interface with neuromodulation,the method comprising an act of: causing one or more processers toexecute instructions encoded on a non-transitory computer-readablemedium, such that upon execution, the one or more processors performoperations of: conditioning signals of interest obtained from a neuraldevice having one or more sensors, wherein the neural device isconfigured to administer neuromodulation stimulation; extracting salientneural features from the conditioned signals of interest; decoding thesalient neural features, providing a mapping between an input neuralfeature space and an output control space for the controllable device;based on the mapping, generating at least one control command for thecontrollable device; causing the controllable device to perform one ormore operations according to the at least one control command; andcausing the neural device to administer neuromodulation stimulation toreinforce operation of the controllable device.
 8. The method as setforth in claim 7, wherein the signals of interest comprise at least oneof neural signals and environmental signals.
 9. The method as set forthin claim 7, wherein the neuromodulation stimulation is one of auditory,visual, and electrical stimulation.
 10. The method as set forth in claim9, wherein the neuromodulation stimulation comprises uniquespatiotemporal amplitude-modulated patterns (STAMPs) of stimulation. 11.The method as set forth in claim 7, wherein the controllable device is aprosthetic limb.
 12. The method as set forth in claim 7, wherein the oneor more neural sensors comprises one or more electrodes configured toperform at least one of sensing and applying stimulation.
 13. A computerprogram product for implementing an enhanced brain-machine interfacewith neuromodulation, the computer program product comprising:computer-readable instructions stored on a non-transitorycomputer-readable medium that are executable by a computer having one ormore processors for causing the processor to perform operations of:conditioning signals of interest obtained from a neural device havingone or more sensors, wherein the neural device is configured toadminister neuromodulation stimulation; extracting salient neuralfeatures from the conditioned signals of interest; decoding the salientneural features, providing a mapping between an input neural featurespace and an output control space for the controllable device; based onthe mapping, generating at least one control command for thecontrollable device; causing the controllable device to perform one ormore operations according to the at least one control command; andcausing the neural device to administer neuromodulation stimulation toreinforce operation of the controllable device.
 14. The computer programproduct as set forth in claim 13, wherein the signals of interestcomprise at least one of neural signals and environmental signals. 15.The computer program product as set forth in claim 13, wherein theneuromodulation stimulation is one of auditory, visual, and electricalstimulation.
 16. The computer program product as set forth in claim 15,wherein the neuromodulation stimulation comprises unique spatiotemporalamplitude-modulated patterns (STAMPs) of stimulation.
 17. The computerprogram product as set forth in claim 13, wherein the controllabledevice is a prosthetic limb.
 18. The computer program product as setforth in claim 13, wherein the one or more neural sensors comprises oneor more electrodes configured to perform at least one of sensing andapplying stimulation.